Rethinking AI: Why accuracy alone isn’t enough in social decision-making

Machine learning is at a crossroads. While accuracy-driven models have propelled technological advancements, their application in social settings demands a more nuanced approach. By integrating welfare economics into the core objectives of machine learning, we can shift from a purely predictive paradigm to one that actively enhances societal well-being.

Machine learning has revolutionized decision-making across various sectors, from healthcare and finance to education and governance. Its ability to predict outcomes with precision has driven its integration into critical areas of human life. However, an inherent flaw in conventional machine learning systems is their primary focus on accuracy rather than social outcomes. As the power of machine learning grows, it is imperative to rethink its fundamental objectives - not just maximizing predictive performance, but ensuring it serves the broader goal of social welfare.

In the paper titled "Machine Learning Should Maximize Welfare, Not (Only) Accuracy," authors Nir Rosenfeld (Technion – Israel Institute of Technology) and Haifeng Xu (University of Chicago) argue for a paradigm shift in machine learning. Published as a preprint under review, the study draws on welfare economics to propose a novel framework that transitions from accuracy-driven learning to welfare-oriented learning, where resources are allocated in a way that maximizes societal benefit.

Shortcomings of accuracy-driven learning

Traditional machine learning models are built to optimize accuracy, but in social settings, accuracy alone does not necessarily translate into beneficial outcomes. In scenarios where machine learning influences real-world decisions - such as university admissions, loan approvals, and job hiring - the allocation of opportunities is inherently tied to economic principles of scarcity. Resources, whether financial aid or employment slots, are limited, and an accuracy-focused system does not account for the social implications of how these resources are distributed.

Consider a school that uses machine learning to determine which students receive targeted educational aid. A model trained purely for accuracy might identify those most likely to succeed with the least amount of support. However, this approach overlooks students on the margins who could benefit the most from assistance. Moreover, it fails to consider that students may act strategically to manipulate their selection outcomes, a phenomenon well-documented in economic literature. This misalignment between prediction accuracy and social good underscores the need for a welfare-aware learning framework.

Integrating welfare economics into machine learning

Rosenfeld and Xu propose that machine learning should adopt principles from welfare economics, a field that studies how resources can be distributed to maximize societal well-being. The key tenets of welfare economics - efficiency and equity - are crucial for developing machine learning models that do not merely predict but also contribute positively to society.

Their proposed framework introduces a hierarchical structure of welfare-aware machine learning, organized into three orders:

1. Order 0 – Allocating Accuracy: The first step is recognizing that predictive accuracy itself is a limited resource. Instead of maximizing uniform accuracy, models should allocate accuracy to individuals based on their relative needs and potential benefits.

2. Order 1 – Incorporating System Decisions: Many machine learning applications influence decision-making processes, from hiring to financial lending. These decisions must be structured within a constrained resource framework, ensuring that they align with equitable distribution goals rather than reinforcing existing inequalities.

3. Order 2 – Enabling User Choices: Users are not passive recipients of machine learning-driven decisions; they respond strategically. This layer accounts for human agency by incorporating user preferences and behaviors into predictive modeling, ensuring that decision-making processes are dynamic and socially aware.

Challenges and the road ahead

Transitioning to a welfare-maximizing machine learning framework presents several challenges. First, designing models that optimize welfare requires defining what constitutes social welfare - a subjective and complex endeavor. Second, accounting for user agency means anticipating strategic behavior, making the learning process more intricate. Third, systems must be structured to prevent unintended negative consequences, such as reinforcing biases or enabling unfair competition.

Furthermore, the role of regulation is critical. Policymakers must establish guidelines that align machine learning objectives with public welfare goals. Whether through subsidies, monitoring mechanisms, or transparency requirements, regulations must ensure that machine learning systems operate within an ethical and socially responsible framework.

Conclusion: A new vision for machine learning

Machine learning is at a crossroads. While accuracy-driven models have propelled technological advancements, their application in social settings demands a more nuanced approach. By integrating welfare economics into the core objectives of machine learning, we can shift from a purely predictive paradigm to one that actively enhances societal well-being.

Rosenfeld and Xu’s work lays the foundation for this transformation, advocating for a future where machine learning is not just a tool for optimizing outcomes, but a mechanism for fostering equitable and efficient social progress.